Home

Row

Tweets Today

32

Tweeters Today

19

#rstats Likes

1162039

#rstats Tweets

122188

Row

Tweet volume

Tweets by Hour of Day

Row

💗 Most Liked Tweet Today

✨ Most Retweeted Tweet Today

🎉 Most Recent

Rankings

Row

Top Tweeters

User Engagement/Tweet
@v_matzek 2453.0
@kaymwilliamson 1864.0
@TheToadLady 1602.5
@kiramhoffman 1138.0
@adastephenson 1086.0
@Kunkakom 923.0
@drhammed 892.0
@SebastienPolis 875.0
@maps4thought 693.0
@kimistry8 689.0

Where Engagement is RT * 2 + Favourite

Network of top tweeters

Relationships in the graph describe replies and quote retweets from the top tweeters that also have the hashtag.

Row

Top Words

Top Locations

Row

Top Hashtags

Hashtag Count
#DataScience 51100
#Python 48771
#IoT 39505
#AI 39331
#Analytics 38964
#MachineLearning 38412
#BigData 37829
#IIoT 33501
#Linux 31727
#Coding 30959

Excluding #rstats and similar variations

Common co-occuring hashtags

Hashtags that occur together, grouped by community detection

Data

Tweets in the current week

---
title: "#rstats Twitter Explorer"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    vertical_layout: scroll
    source_code: embed
    theme:
      version: 4
      bootswatch: yeti
    css: styles/main.css
---

```{r load_proj, include=FALSE}
devtools::load_all()
```

```{r load_packages, include=FALSE, cache=TRUE}
library(flexdashboard)
library(rtweet)
library(dplyr)
library(stringr)
library(tidytext)
library(lubridate)
library(echarts4r)
library(DT)

rstats_tweets <- read_twitter_csv("data/rstats_tweets.csv.gz") %>%
  mutate(created_at = as_datetime(created_at))
```


```{r time_data, include=FALSE, cache=TRUE}
count_timeseries <- rstats_tweets %>%
  ts_data(by = "hours")

tweets_week <- rstats_tweets %>%
  filter(date(created_at) %within% interval(floor_date(today(), "week"), today()))

tweets_today <- rstats_tweets %>%
  filter(date(created_at) == today())
```


```{r numbers, include=FALSE, cache=TRUE}
number_of_unique_tweets <- get_unique_value(rstats_tweets, text)

number_of_unique_tweets_today <-
  get_unique_value(tweets_today, text)

number_of_tweeters_today <- get_unique_value(tweets_today, user_id)

number_of_likes <- rstats_tweets %>%
  pull(favorite_count) %>%
  sum()
```


```{r rankings_data, include=FALSE, cache=TRUE}
top_tweeters <- rstats_tweets %>%
  group_by(user_id, screen_name, profile_url, profile_image_url) %>%
  summarize(engagement = (sum(retweet_count) * 2 + sum(favorite_count)) / n()) %>%
  ungroup() %>%
  slice_max(engagement, n = 10, with_ties = FALSE)

top_tweeters_format <- top_tweeters %>% 
  mutate(
    profile_url = stringr::str_glue("https://twitter.com/{screen_name}"),
    screen_name = stringr::str_glue('@{screen_name}'),
    engagement = formattable::color_bar("#a3c1e0", formattable::proportion)(engagement)
  ) %>%
  select(screen_name, engagement)

top_hashtags <- rstats_tweets %>%
  tidyr::separate_rows(hashtags, sep = " ") %>%
  count(hashtags) %>%
  filter(!(hashtags %in% c("rstats", "RStats"))) %>%
  slice_max(n, n = 10, with_ties = FALSE) %>%
  mutate(
    number = formattable::color_bar("plum", formattable::proportion)(n),
    hashtag = stringr::str_glue(
      '#{hashtags}'
    ),
  ) %>%
  select(hashtag, number)

word_banlist <-  c("t.co", "https", "rstats")
top_words <- rstats_tweets %>%
  select(text) %>%
  unnest_tokens(word, text) %>%
  anti_join(stop_words) %>%
  filter(!(word %in% word_banlist)) %>%
  filter(nchar(word) >= 4) %>% 
  count(word, sort = TRUE) %>%
  slice_max(n, n = 10, with_ties = FALSE) %>%
  select(word, n)

top_co_hashtags <- rstats_tweets %>% 
  unnest_tokens(bigram, hashtags, token = "ngrams", n = 2) %>% 
  tidyr::separate(bigram, c("word1", "word2"), sep = " ") %>% 
  filter(!word1 %in% c(stop_words$word, word_banlist)) %>% 
  filter(!word2 %in% c(stop_words$word, word_banlist)) %>% 
  count(word1, word2, sort = TRUE) %>% 
  filter(!is.na(word1) & !is.na(word2)) %>% 
  slice_max(n, n = 100, with_ties = FALSE)

top_locations <- rstats_tweets %>%
  filter(!is.na(location) & location != "#rstats") %>%
  distinct(user_id, .keep_all = TRUE) %>%
  mutate(location = str_replace_all(location, "London$", "London, England")) %>% 
  count(location) %>%
  slice_max(n, n = 10, with_ties = FALSE)
```


Home {data-icon="ion-home"}
====

Row
-----------------------------------------------------------------------

### Tweets Today

```{r tweets_today}
valueBox(number_of_unique_tweets_today, icon = "fa-comment-alt", color = "plum")
```

### Tweeters Today

```{r tweeters_today}
valueBox(number_of_tweeters_today, icon = "fa-user", color = "peachpuff")
```

### #rstats Likes

```{r likes}
valueBox(number_of_likes, icon = "fa-heart", color = "palevioletred")
```

### #rstats Tweets

```{r unique_tweets}
valueBox(number_of_unique_tweets, icon = "fa-comments", color = "mediumorchid")
```

Row {.tabset .tabset-fade}
-----------------------------------------------------------------------

### Tweet volume

```{r tweet_volume}
plot_tweet_volume(count_timeseries)
```

### Tweets by Hour of Day

```{r tweets_by_hour}
plot_tweet_by_hour(rstats_tweets)
```

Row
-----------------------------------------------------------------------

### 💗 Most Liked Tweet Today {.tweet-box}

```{r most_liked}
most_liked_url <- tweets_today %>%
  slice_max(favorite_count, with_ties = FALSE)

get_tweet_embed(most_liked_url$screen_name, most_liked_url$status_id)
```

### ✨ Most Retweeted Tweet Today {.tweet-box}

```{r most_rt}
most_retweeted <- tweets_today %>%
  slice_max(retweet_count, with_ties = FALSE)

get_tweet_embed(most_retweeted$screen_name, most_retweeted$status_id)
```

### 🎉 Most Recent {.tweet-box}

```{r most_recent}
most_recent <- tweets_today %>%
  slice_max(created_at, with_ties=FALSE)

get_tweet_embed(most_recent$screen_name, most_recent$status_id)
```

Rankings {data-icon="ion-arrow-graph-up-right"}
=========

Row
-----------------------------------------------------------------------

### Top Tweeters

```{r top_tweeters}
top_tweeters_format %>%
  knitr::kable(
    format = "html",
    escape = FALSE,
    align = "cll",
    col.names = c("User", "Engagement/Tweet "),
    table.attr = 'class = "table"'
  )
```

Where Engagement is `RT * 2 + Favourite`

### Network of top tweeters

Relationships in the graph describe replies and quote retweets from the top tweeters
that also have the hashtag.

```{r top_tweeters_net}
edgelist <-
  network_data(rstats_tweets %>% unflatten(), "reply,quote")
nodelist <- attr(edgelist, "idsn") %>%
  bind_cols()

top_edges <- edgelist %>%
  filter((from %in% top_tweeters$user_id) |
           (to %in% top_tweeters$user_id))

top_nodes <- nodelist %>%
  filter((id %in% top_edges$from) | (id %in% top_edges$to)) %>%
  mutate(is_top = ifelse((id %in% top_tweeters$user_id), "yes", "no"),
         size = 10)

e_charts() %>%
  e_graph() %>%
  e_graph_nodes(top_nodes, id, sn, size, category = is_top, legend = FALSE) %>%
  e_graph_edges(top_edges, from, to) %>%
  e_tooltip()
```

Row
-----------------------------------------------------------------------

### Top Words

```{r top_words}
top_words %>%
  e_charts(word) %>%
  e_bar(n, legend = FALSE) %>% 
  e_x_axis(
    axisLabel = list(
      interval = 0L,
      rotate = 30
    )
  ) %>%
  e_toolbox_feature("saveAsImage") %>%
  e_axis_labels(y = "Number of occurrences")
```

### Top Locations

```{r top_locations}
top_locations %>% 
  mutate(location = str_wrap(location, 9)) %>% 
  e_charts(location) %>% 
  e_bar(n, legend = FALSE) %>% 
  e_x_axis(
    axisLabel = list(
      interval = 0L,
      rotate = 30
    )
  ) %>%
  e_toolbox_feature("saveAsImage") %>%
  e_axis_labels(y = "Number of users from location")
```


Row
-----------------------------------------------------------------------

### Top Hashtags

```{r top_hashtags}
top_hashtags %>%
  knitr::kable(
    format = "html",
    escape = FALSE,
    align = "cll",
    col.names = c("Hashtag", "Count"),
    table.attr = 'class = "table"'
  )
```

Excluding `#rstats` and similar variations

### Common co-occuring hashtags

Hashtags that occur together, grouped by community detection

```{r co_hashtags}
top_co_hash_nodes <- tibble(
  nodes = c(top_co_hashtags$word1, top_co_hashtags$word2)
) %>% 
  distinct()

e_chart() %>% 
  e_graph() %>% 
  e_graph_nodes(top_co_hash_nodes, nodes, nodes, nodes) %>% 
  e_graph_edges(top_co_hashtags, word1, word2) %>% 
  e_modularity()
```


Data {data-icon="ion-stats-bars"}
==============

### Tweets in the current week {.datatable-container}

```{r datatable}
tweets_week %>%
  select(
    status_url,
    created_at,
    screen_name,
    text,
    retweet_count,
    favorite_count,
    mentions_screen_name
  ) %>%
  mutate(
    status_url = stringr::str_glue("On Twitter")
  ) %>%
  datatable(
    .,
    extensions = "Buttons",
    rownames = FALSE,
    escape = FALSE,
    colnames = c("Timestamp", "User", "Tweet", "RT", "Fav", "Mentioned"),
    filter = 'top',
    options = list(
      columnDefs = list(list(
        targets = 0, searchable = FALSE
      )),
      lengthMenu = c(5, 10, 25, 50, 100),
      pageLength = 10,
      scrollY = 600,
      scroller = TRUE,
      dom = '<"d-flex justify-content-between"lBf>rtip',
      buttons = list('copy', list(
        extend = 'collection',
        buttons = c('csv', 'excel'),
        text = 'Download'
      ))
    )
  )
```